Biomedical subjects

Sophie Deneve

Publications and source records attributed to Sophie Deneve.

4 recordsLinked to original sources

PubMed2003

Basis functions for object-centered representations.

In an object-centered representation, the position of the subparts of an object are encoded with respect to a set of axes and an origin centered on the object. Several physiological and neuropsychological results support the existence of such representations in humans and monkeys. An explicit representation would involve neurons with invariant response properties in object-centered coordinates. We consider an alternative scheme using basis functions in which the cells have retinotopic receptive fields modulated by the orientation of the object and task-related signals. We show that this alternative is consistent with single-cell data, is computationally efficient, and accounts for object-centered hemineglect, a syndrome observed in humans after fronto-parietal lesions.

Algorithms↗

PubMed2002

A computational perspective on the neural basis of multisensory spatial representations.

We argue that current theories of multisensory representations are inconsistent with the existence of a large proportion of multimodal neurons with gain fields and partially shifting receptive fields. Moreover, these theories do not fully resolve the recoding and statistical issues involved in multisensory integration. An alternative theory, which we have recently developed and review here, has important implications for the idea of 'frame of reference' in neural spatial representations. This theory is based on a neural architecture that combines basis functions and attractor dynamics. Basis function units are used to solve the recoding problem, whereas attractor dynamics are used for optimal statistical inferences. This architecture accounts for gain fields and partially shifting receptive fields, which emerge naturally as a result of the network connectivity and dynamics.

Animals↗

PubMedDate not supplied

Optimal computation with attractor networks.

We investigate the ability of multi-dimensional attractor networks to perform reliable computations with noisy population codes. We show that such networks can perform computations as reliably as possible--meaning they can reach the Cramér-Rao bound--so long as the noise is small enough. "Small enough" depends on the properties of the noise, especially its correlational structure. For many correlational structures, noise in the range of what is observed in the cortex is sufficiently small that biologically plausible networks can compute optimally. We demonstrate that this result applies to computations that involve cues of varying reliability, such as the position of an object on the retina in bright versus dim light.

Animals↗

PubMedDate not supplied

Bayesian multisensory integration and cross-modal spatial links.

Our perception of the word is the result of combining information between several senses, such as vision, audition and proprioception. These sensory modalities use widely different frames of reference to represent the properties and locations of object. Moreover, multisensory cues come with different degrees of reliability, and the reliability of a given cue can change in different contexts. The Bayesian framework--which we describe in this review--provides an optimal solution to deal with this issue of combining cues that are not equally reliable. However, this approach does not address the issue of frames of references. We show that this problem can be solved by creating cross-modal spatial links in basis function networks. Finally, we show how the basis function approach can be combined with the Bayesian framework to yield networks that can perform optimal multisensory combination. On the basis of this theory, we argue that multisensory integration is a dialogue between sensory modalities rather that the convergence of all sensory information onto a supra-modal area.

Animals↗